LogicForest: Logic Forest

Two classification ensemble methods based on logic regression models. LogForest() uses a bagging approach to construct an ensemble of logic regression models. LBoost() uses a combination of boosting and cross-validation to construct an ensemble of logic regression models. Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

Version: 2.1.1
Depends: R (≥ 2.10)
Imports: LogicReg, methods
Suggests: data.table, knitr, rmarkdown
Published: 2024-03-13
DOI: NA
Author: Bethany Wolf [aut], Melica Nikahd [ctb, cre], Madison Hyer [ctb]
Maintainer: Melica Nikahd <melica.nikahd at osumc.edu>
License: GPL-3
NeedsCompilation: no
CRAN checks: LogicForest results

Documentation:

Reference manual: LogicForest.pdf
Vignettes: Introduction to Logic Forest

Downloads:

Package source: LogicForest_2.1.1.tar.gz
Windows binaries: r-devel: LogicForest_2.1.1.zip, r-release: LogicForest_2.1.1.zip, r-oldrel: LogicForest_2.1.1.zip
macOS binaries: r-release (arm64): LogicForest_2.1.1.tgz, r-oldrel (arm64): LogicForest_2.1.1.tgz, r-release (x86_64): LogicForest_2.1.1.tgz, r-oldrel (x86_64): LogicForest_2.1.1.tgz
Old sources: LogicForest archive

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